Prediksi Laju Inflasi di Jawa Timur Menggunakan Model N-BEATS dan Optimasi Optuna

Prediction of Inflation Rate in East Java Using the N-BEATS Model and Optuna Optimization

Authors

  • Mohammad Nizar Riswanda Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Trimono Trimono Universitas Pembangunan Nasional “Veteran” Jawa Timur
  • Wahyu Syaifullah Jauharis Saputra Universitas Pembangunan Nasional “Veteran” Jawa Timur

Keywords:

Inflasi, Jawa Timur, N-BEATS, Optuna, Prediksi

Abstract

Inflasi merupakan indikator penting yang memengaruhi kestabilan dan pertumbuhan ekonomi suatu wilayah. Prediksi inflasi yang akurat sangat dibutuhkan guna mendukung perumusan kebijakan ekonomi yang tepat. Penelitian ini mengusulkan penggunaan model N-BEATS (Neural Basis Expansion Analysis for Time Series) yang dioptimalkan dengan Optuna untuk memprediksi inflasi di Provinsi Jawa Timur. Data yang digunakan berupa deret waktu univariat, yaitu laju inflasi bulanan dari Januari 2005 hingga Desember 2024, yang diperoleh dari Badan Pusat Statistik (BPS). Evaluasi performa model dilakukan menggunakan metrik Mean Absolute Percentage Error (MAPE). Berbeda dengan model tradisional seperti ARIMA dan LSTM, N-BEATS mengandalkan jaringan saraf feedforward dengan arsitektur blok residual yang mampu melakukan rekonstruksi masa lalu (backcast) dan prediksi masa depan (forecast). Optimasi hyperparameter melalui Optuna berhasil meningkatkan akurasi model secara signifikan. Hasil Penelitian menunjukkan bahwa N-BEATS teroptimasi mencapai MAPE sebesar 8,97%, lebih baik dibandingkan N-BEATS dasar (11,05%), ARIMA (16,95%), dan LSTM (12,23%). Temuan ini mengindikasikan bahwa pendekatan N-BEATS dengan Optuna efektif dalam meningkatkan akurasi prediksi inflasi dan dapat menjadi alat bantu penting bagi perencanaan ekonomi di tingkat daerah.

Downloads

Download data is not yet available.

References

E. F. B. Simanungkalit, “Pengaruh inflasi terhadap pertumbuhan ekonomi di Indonesia,” Journal of Management, vol. 13, no. 3, pp. 327–340, 2020, doi: 10.35508/jom.v13i3.3311.

A. Salim and A. Purnamasari, “Pengaruh inflasi terhadap pertumbuhan ekonomi Indonesia,” Ekonomica Sharia: Jurnal Pemikiran dan Pengembangan Ekonomi Syariah, vol. 7, no. 1, pp. 17–28, 2021.

A. N. Ramadanti, E. I. Zulfa, N. M. Sunariadi, and D. C. R. Novitasari, “Peramalan pergerakan inflasi di Jawa Timur dengan menggunakan metode triple exponential smoothing,” J. Matematika Sains dan Teknologi, vol. 22, no. 2, pp. 40–49, Mar. 2022, doi: 10.33830/jmst.v22i2.1346.2021.

S. N. Fadhilah, F. Indriyani, and S. Suharsono, “Pengaruh inflasi, pertumbuhan ekonomi, jumlah penduduk terhadap kesejahteraan dengan ZIS sebagai variabel moderasi,” Almaal, vol. 3, no. 2, p. 154, Jan. 2022, doi: 10.31000/almaal.v3i2.4630.

A. Safira, R. A. Dhiya’ulhaq, I. Fahmiyah, and M. Ghani, “Spatial impact on inflation of Java Island prediction using autoregressive integrated moving average (ARIMA) and generalized space-time ARIMA (GSTARIMA),” MethodsX, vol. 13, p. 102867, Dec. 2024, doi: 10.1016/j.mex.2024.102867.

M. A. S. Izza, F. L. Wachdah, and M. Yasin, “Analisis pertumbuhan ekonomi di provinsi Jawa Timur tahun 2022,” Trending, vol. 1, no. 3, pp. 42–50, Jun. 2023, doi: 10.30640/trending.v1i3.1122.

M. L. Ashari and M. Sadikin, “Prediksi data transaksi penjualan time series menggunakan regresi LSTM,” J. Nas. Pendidik. Teknik. Inform., vol. 9, no. 1, p. 1, Apr. 2020, doi: 10.23887/janapati.v9i1.19140.

M. Idhom, A. Fauzi, T. Trimono, and P. Riyantoko, “Time series regression: Prediction of electricity consumption based on number of consumers at National Electricity Supply Company,” TEM Journal, pp. 1575–1581, Aug. 2023, doi: 10.18421/tem123-39.

G. Alomani, M. Kayid, and M. F. Abd El-Aal, “Global inflation forecasting and uncertainty assessment: Comparing ARIMA with advanced machine learning,” Journal of Radiation Research and Applied Sciences, vol. 18, no. 2, p. 101402, Jun. 2025, doi: 10.1016/j.jrras.2025.101402.

T. L. Narayanaa, R. R. Skandarsini, S. J. Ida, S. R. Sabapathy, and P. Nanthitha, “Inflation prediction: A comparative study of ARIMA and LSTM models across different temporal resolutions,” in 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India, Dec. 2023, pp. 1390–1395, doi: 10.1109/icimia60377.2023.10425970.

B. Subburaj and A. Agrawal, “Prophet and NeuralProphet compared with Indian inflation data,” in 2024 11th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, Mar. 2024, pp. 205–210, doi: 10.1109/spin60856.2024.10512170.

K. Xu, J. Zhang, J. Huang, H. Tan, X. Jing, and T. Zheng, “Forecasting visitor arrivals at tourist attractions: A time series framework with the N-BEATS for sustainable tourism,” Sustainability, vol. 16, no. 18, p. 8227, Sep. 2024, doi: 10.3390/su16188227.

B. N. Oreshkin, G. Dudek, P. Pe?ka, and E. Turkina, “N-BEATS neural network for mid-term electricity load forecasting,” Applied Energy, vol. 293, p. 116918, Jul. 2021, doi: 10.1016/j.apenergy.2021.116918.

M. Melina et al., “Comparative analysis of time series forecasting models using ARIMA and neural network autoregression methods,” Barekeng: J. Math. & App., vol. 18, no. 4, pp. 2563–2576, Oct. 2024, doi: 10.30598/barekengvol18iss4pp2563-2576.

R. Dhanalakshmi, R. Harsh, and S. B. Prathiba, “Predicting the Price of Stock Using Deep Learning Algorithms,” in 2023 International Conference on System, Computation, Automation and Networking (ICSCAN), PUDUCHERRY, India: IEEE, Nov. 2023, pp. 1–6. doi: 10.1109/ICSCAN58655.2023.10395609.

B. S. Naik et al., “Stock price forecasting using N-BEATS deep learning architecture,” J. Sci. Res. Rep., vol. 30, no. 9, pp. 483–494, Sep. 2024, doi: 10.9734/jsrr/2024/v30i92373.

M. H. Zein, N. Yudistira, and P. P. Adikara, “Indonesian stock price prediction using neural basis expansion analysis for interpretable time series method,” JICT, vol. 23, no. 3, pp. 361–392, Jul. 2024, doi: 10.32890/jict2024.23.3.1.

A. Bulatov, “Forecasting bitcoin prices using N-BEATS deep learning architecture,” Sheridan College Student Thesis, 2020.

S. Martín-Suazo et al., “Deep learning methods for multi-horizon long-term forecasting of harmful algal blooms,” Knowledge-Based Systems, vol. 301, p. 112279, Oct. 2024, doi: 10.1016/j.knosys.2024.112279.

A. K. P. Anil and U. K. Singh, “An optimal solution to the overfitting and underfitting problem of healthcare machine learning models,” J Syst Eng Inf Technol, vol. 2, no. 2, pp. 77–84, Oct. 2023, doi: 10.29207/joseit.v2i2.5460.

S. Watanabe and F. Hutter, “c-TPE: Tree-structured Parzen estimator with inequality constraints for expensive hyperparameter optimization,” in Proc. 32nd Int. Joint Conf. Artificial Intelligence (IJCAI), Macau, SAR China, Aug. 2023, pp. 4371–4379, doi: 10.24963/ijcai.2023/486.

T. Trimono, A. Sonhaji, and U. Mukhaiyar, “Forecasting farmer exchange rate in Central Java Province using vector integrated moving average,” Medstat, vol. 13, no. 2, pp. 182–193, Dec. 2020, doi: 10.14710/medstat.13.2.182-193.

T. M. Fahrudin, P. A. Riyantoko, K. M. Hindrayani, and I. G. S. Mas Diyasa, “Exploratory data analysis pada kasus COVID-19 di Indonesia menggunakan HiveQL dan Hadoop environment,” Santika, vol. 1, pp. 115–123, Nov. 2020, doi: 10.33005/santika.v1i0.32.

T. Ferdousi, L. W. Cohnstaedt, and C. M. Scoglio, “A windowed correlation-based feature selection method to improve time series prediction of dengue fever cases,” IEEE Access, vol. 9, pp. 141210–141222, 2021, doi: 10.1109/ACCESS.2021.3120309.

F. Nurcakhyadi and A. Hermawan, “Optimizing windowing techniques to improve the accuracy of artificial neural networks in predicting outpatient visits,” Ilk. J. Ilm., vol. 16, no. 2, pp. 172–183, Aug. 2024, doi: 10.33096/ilkom.v16i2.2254.172-183.

D. A. Prasetya, A. P. Sari, M. Idhom, and A. Lisanthoni, “Optimizing clustering analysis to identify high-potential markets for Indonesian tuber exports,” Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics, vol. 7, no. 1, pp. 113–122, 2025, doi: 10.35882/skzqbd57.

A. Karamchandani, A. Mozo, S. Vakaruk, S. Gómez-Canaval, J. E. Sierra-García, and A. Pastor, “Using N-BEATS ensembles to predict automated guided vehicle deviation,” Appl Intell, vol. 53, no. 21, pp. 26139–26204, Nov. 2023, doi: 10.1007/s10489-023-04820-0.

B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting,” ICLR, 2020.

Z. A. Riyadi, M. H. Husen, L. A. Lubis, and T. K. Ridwan, “The implementation of TPE-Bayesian hyperparameter optimization to predict shear wave velocity using machine learning: Case study from X field in Malay Basin,” Petroleum and Coal, vol. 64, no. 2, pp. 467–488, 2022.

G. Rong et al., “Comparison of tree-structured Parzen estimator optimization in three typical neural network models for landslide susceptibility assessment,” Remote Sensing, vol. 13, no. 22, p. 4694, Nov. 2021, doi: 10.3390/rs13224694.

M. M. Miah, K. K. Hyun, S. P. Mattingly, and H. Khan, “Estimation of daily bicycle traffic using machine and deep learning techniques,” Transportation, vol. 50, no. 5, pp. 1631–1684, Oct. 2023, doi: 10.1007/s11116-022-10290-z.

J.-P. Lai, Y.-L. Lin, H.-C. Lin, C.-Y. Shih, Y.-P. Wang, and P.-F. Pai, “Tree-based machine learning models with Optuna in predicting impedance values for circuit analysis,” Micromachines, vol. 14, no. 2, p. 265, Jan. 2023, doi: 10.3390/mi14020265.

A. T. Damaliana, K. M. Hindrayani, and T. M. Fahrudin, “Hybrid Holt Winter-Prophet method to forecast the number of foreign tourist arrivals through Bali’s Ngurah Rai Airport,” IJDASEA Int’l J. of DA. DE. DA., vol. 3, no. 2, pp. 21–32, May 2024, doi: 10.33005/ijdasea.v3i2.8.

N. K. Khairunisa and P. Hendikawati, “Long short-term memory and gated recurrent unit modeling for stock price forecasting,” JMSK, vol. 21, no. 1, pp. 321–333, Sep. 2024, doi: 10.20956/j.v21i1.35930.

U. Orji and E. Ukwandu, “Machine learning for an explainable cost prediction of medical insurance,” Machine Learning with Applications, vol. 15, p. 100516, Mar. 2024, doi: 10.1016/j.mlwa.2023.100516.

S. Sengupta, T. Chakraborty, and S. K. Singh, “Forecasting CPI inflation under economic policy and geopolitical uncertainties,” International Journal of Forecasting, p. S016920702400092X, Sep. 2024, doi: 10.1016/j.ijforecast.2024.08.005

Published

2025-07-31

How to Cite

Riswanda, M. N., Trimono, T., & Saputra, W. S. J. (2025). Prediksi Laju Inflasi di Jawa Timur Menggunakan Model N-BEATS dan Optimasi Optuna: Prediction of Inflation Rate in East Java Using the N-BEATS Model and Optuna Optimization . MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5(3). Retrieved from https://journal.irpi.or.id/index.php/malcom/article/view/2141